GI-Forum SS 2014

We will present an algebra to process and analyze field based spatio-temporal geographical data. Our algebra is implemented in GRASS GIS that was recently improved to a full featured temporal GIS. The temporal enabled GRASS GIS (TGRASS) supports now the management, processing and analysis of space time datasets, which are an equivalent of spatio-temporal fields. Space time datasets for vector, raster and 3D raster are supported in TGRASS. A specific property of space time datasets is that the time dimension may have an arbitrary structure, hence temporal gaps, inclusion and overlapping is allowed. The core of TGRASS is the GRASS GIS temporal framework, a sophisticated temporal GIS Python API. Using the GRASS GIS temporal framework allowed us to design and implement a spatio-temporal algebra, that supports operators to perform spatial and temporal operations (union, intersection, disjoint union, ...) on space time vector, raster and 3D raster datasets. These operators support the definition of temporal topological relationships, that specify the temporal relations between different space time datasets.
We will introduce the concept of the temporal algebra first by explaining all available temporal operations. These operations include temporal shifting, buffering and selection that is based on topological relations, date and time expressions. The temporal algebra is the basis of the spatio-temporal vector and raster algebra that extents the temporal algebra with boolean operations like temporal union or temporal intersection.
Second we will introduce the spatio-temporal vector algebra that supports spatial boolean operations and buffer operations on vector data in addition to the temporal operations. Following the vector algebra we will introduce the spatio-temporal raster algebra that support common spatial map algebra operations like addition, substraction, multiplication, division and modulus as well as spatio-temporal neighborhood operations, functions and constants. Our algebraic approach allows the specification of spatial and temporal operations as well as temporal topological relationships in a single operator. That allows us to perform complex spatio-temporal operations on space time datasets using simple algebraic expressions. Our implementation allows the very efficient processing of large space time datasets, since the decision which part of the space time datasets should be processed is based on a spatio-temporal topology index build upon a R*-tree. We will demonstrate the capabilities of our spatio-temporal algebra using several examples.

With increasing amounts and variety of and access to open data collected in our environment, the distance between those who understand the data observation processes and those who analyse the data increases. This enlarges the risk of carrying out operations that are not meaningful, such as summing temperature values or interpolating coal power plant emissions. To avoid the need for tables with permitted analysis procedure for every phenomenon, we attempt to categorize phenomena first. Combining the ideas of Stevens' (1946) one-dimensional measurement scale types with those of spatial and temporal reference systems, we construct compound reference systems that address how space, time, quality, and entity cohere. What we find formalizes ideas established in spatial statistics over three decades ago, but has not been reflected well in spatial data standardization efforts, or spatial data base design. The talk will discuss the potential of these findings, implementation challenges, and their value for relational and array data bases for spatio-temporal data.

In this talk a summary of recent trend shifts detection studies will be presented. We will zoom in from global scale analysis of vegetation activity into (1) the Sahel and finalise with (2) tropical deforestation monitoring.

First, the fact that soil moisture drives vegetation growth in semi-arid ecosystems like the Sahel helps to disentangle potential data artefacts from real changes driven by climate and human influences. Monotonic decreasing soil moisture trends have been reported, while rainfall has been increasing since the Sahel drought of the 1970s and early 1980s. By accounting for structural changes in soil moisture and vegetation activity trends, we aim to find answers for the increasing rainfall and decreasing soil moisture ambiguity. Trends, anomalies and associated ecosystem impacts can now be studied with a combination of long-term soil moisture (1978-2011, Soil Moisture Climate Change Initiative) and vegetation activity records (1982-2011, NDVI3g).

Second, satellite remote sensing enables cost-effective and accurate monitoring at frequent time steps over large areas. Yet, generic methods to detect disturbances within newly captured satellite images are lacking. We propose a multi-purpose time-series-based disturbance detection approach that identifies and models stable historical variation to enable change detection within newly acquired data. We illustrate the potential of the method for near real-time deforestation monitoring in Ethiopia and Vietnam.

Links:
http://bfast.r-forge.r-project.org/
http://wageningenur.nl/changemonitor

Key references:
de Jong, R., Verbesselt, J., Zeileis, A. & Schaepman, M. Shifts in Global Vegetation Activity Trends. Remote Sensing 5, 1117–1133 (2013).
Verbesselt, J., Zeileis, A. & Herold, M. Near real-time disturbance detection using satellite image time series. Remote Sensing Of Environment 123, 98–108 (2012).
Verbesselt, J., Hyndman, R., Newnham, G. & Culvenor, D. Detecting trend and seasonal changes in satellite image time series. Remote Sensing Of Environment 114, 106–115 (2010).

In this talk I will analyse the research domain of GIScience in an attempt to understand the multifaceted scientific nature of this academic field. Based on a recent article of Blaschke and Merschdorf [1], I will emphasize its multidisciplinary and multiparadigmatic nature. The talk will particularly address the concept of (Geographic) Object-based Image Analysis – GEOBIA or OBIA in short. The term OBIA is used for generic image processing tasks while GEOBIA’s aims at the generation of geographic information (in GIS-ready format) from which new spatial knowledge can be obtained. I will outline that GEOBIA methods and methodologies can structure the complexity of our environment and, likewise, complexity of measurements into scaled representations for further analysis and monitoring tasks. Some key properties of GEOBIA make it distinct and increasingly being considered as a new paradigm in remote sensing and GIScience. Some of the examples will highlight a full integration of remote sensing and GIS-like analysis in a contextually oriented way which some scientists call ‘geo-intelligence’.
[1] www.tandfonline.com/doi/full/10.1080/15230406.2014.905755#.U3npOCgQej8

Biographical Sketch
Thomas Blaschke is a Professor for Geoinformatics at the University of Salzburg, Austria, Director of the Doctoral College GIScience and Head of the Research Studio iSPACE. His research interests include methodological issues of the integration of GIS and remote sensing for environmental modeling, in particular integrating methods and domain knowledge into spatial analysis and GIS-based spatial decision support system. His academic record yields 320+ scientific publications including 70 journal publications. He is author, co-author or editor of 17 books and received several academic awards including the Christian-Doppler Prize 1995.

The lecture discusses maps and mapping from the perspective of political geography and critical cartography. Following a Foucaultian line of argumentation maps and the tools and institutions which create them can be seen as parts of a powerful discourse producing spatial knowledge within society. Within this setting, the map can be regarded as a specific set of power-knowledge claim (Crampton and Krygier 2006, Glasze and Mose 2011).
The presentation gives an overview into some concepts in political geography (radical approaches, critical geopolitics) and critical cartography which might be useful to analyze and “deconstruct” the political geographies of maps and the political impact of mapping in geopolitical contexts. Additionally it presents some examples concerning the subtle visual power of maps and the “territorial trap” in such geopolitical representations.

Trajectories have become a major type of geographic data, both in practice and from the research perspective. The analysis of trajectory data leads to understanding of movement by segmentation, pattern detection, and other analysis methods. These methods need to be properly defined and then implemented using efficient algorithms.

In this presentation we take the algorithmic approach and apply it to the trajectory analysis tasks of segmentation and computing the grouping structure. The algorithmic approach involves first defining the problem fully, usually after abstracting it to its essential aspects. Second, the problem is solved with algorithmic methods exactly how it was defined.

We will see that a trajectory is more than just a set of time-sampled locations, and that using interpolated locations in between is necessary to get correct output. We will also see the difference between segmentation on absolute criteria and segmentation on relative criteria, and argue that relative criteria are better because they avoid over-segmentation. For defining the trajectory grouping structure we will see that tools from computational topology are useful.