Biology of Applied Digital Ecosystems

A primary motivation for our research in Digital Ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex, dynamic problems. However, the biological processes that contribute to these properties have not been made explicit in Digital Ecosystems research. Here, we discuss how biological properties contribute to the self-organising features of biological ecosystems, including population dynamics, evolution, a complex dynamic environment, and spatial distributions for generating local interactions. The potential for exploiting these properties in artificial systems is then considered. We suggest that several key features of biological ecosystems have not been fully explored in existing digital ecosystems, and discuss how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, with measures originating from theoretical ecology, to confirm its likeness to a biological ecosystem. Including the responsiveness to requests for applications from the user base, as a measure of the 'ecological succession' (development).Comment: 9 pages, 4 figure, conferenc

CCI Land Cover Pre-processing. Challenges of pre-processing for Land Cover classification

The CCI Land Cover project aims to generate a multi-sensor global land cover (ECV) dataset, using ENVISAT MERIS and ASAR as well as SPOT-VGT data. It will produce three combined land cover products for the years 2000, 2005 and 2010. The project builds upon the state-of-the-art technology from the GlobCover project. The processing chain contains 2 steps: pre-processing and classification. The work includes the already existing methods of validation, performance assessment and a detailed documentation of the processing procedures. Pre-processing includes the following steps: geometric and radiometric correction of the input data, pixel identification, atmospheric correction, compositing and mosaicing

CCI Land Cover Pre-processing. Challenges of pre-processing for Land Cover classification

The CCI Land Cover project aims to generate a multi-sensor global land cover (ECV) dataset, using ENVISAT MERIS and ASAR as well as SPOT-VGT data. It will produce three combined land cover products for the years 2000, 2005 and 2010. The project builds upon the state-of-the-art technology from the GlobCover project. The processing chain contains 2 steps: pre-processing and classification. The work includes the already existing methods of validation, performance assessment and a detailed documentation of the processing procedures. Pre-processing includes the following steps: geometric and radiometric correction of the input data, pixel identification, atmospheric correction, compositing and mosaicing

Proba-V cloud detection Round Robin: Validation results and recommendations

This paper discusses results from 12 months of a Round Robin exercise aimed at the inter-comparison of different cloud detection algorithms for Proba-V. Clouds detection is a critical issue for satellite optical remote sensing, since potential errors in cloud masking directly translates into significant uncertainty in the retrieved downstream geophysical products. Cloud detection is particularly challenging for Proba-V due to the presence of a limited number of spectral bands and the lack of thermal infrared bands. The main objective of the project was the inter-comparison of several cloud detection algorithms for Proba-V over a wide range of surface types and environmental conditions. Proba-V Level 2a products have been distributed to six different algorithm providers representing companies and research institutes in several European countries. The considered cloud detection approaches are based on different strategies: Neural Network, Discriminant Analysis, Multi-spectral and Multi-textural Thresholding, Self-Organizing Feature Maps, Dynamic Thresholding, and physically-based retrieval of Cloud Optical Thickness. The results from all algorithms were analysed and compared against a reference dataset, consisting of a large number (more than fifty thousands) of visually classified pixels. The quality assessment was performed according to a uniform methodology and the results provide clear indication on the potential best-suited approach for next Proba-V cloud detection algorithm

Dual-phase evolution in complex adaptive systems

Understanding the origins of complexity is a key challenge in many sciences. Although networks are known to underlie most systems, showing how they contribute to well-known phenomena remains an issue. Here, we show that recurrent phase transitions in network connectivity underlie emergent phenomena in many systems. We identify properties that are typical of systems in different connectivity phases, as well as characteristics commonly associated with the phase transitions. We synthesize these common features into a common framework, which we term dual-phase evolution (DPE). Using this framework, we review the literature from several disciplines to show that recurrent connectivity phase transitions underlie the complex properties of many biological, physical and human systems. We argue that the DPE framework helps to explain many complex phenomena, including perpetual novelty, modularity, scale-free networks and criticality. Our review concludes with a discussion of the way DPE relates to other frameworks, in particular, self-organized criticality and the adaptive cycle