Publikasjoner
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2012
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We exploit two recently developed informationtheoretic quantities (or measures) designed to quantify information content and complexity of ordered data (time series), respectively. Both are based on order statistics of given data sets, and probe into the shortterm structure of the data only due to finite length restrictions. Their usage requires fixing one parameter, word length or order depth D. The information measure is the orderbased Shannon entropy HS, and the complexity measures is the JensenShannon divergence CJS. The latter requires a chosen reference distribution, i.e. CJS represents a class of measures. Entropy HS and complexity CJS of data series may be represented against each other in a twodimensional diagram which we will refer to as ComplexityEntropy Causality Plane, or CECP. Very long realizations of classic stochastic processes and chaotic deterministic maps each obey one location in the CECP, specific for the process. This can be used to differentiate chaos from correlated noise (Rosso et al. 2007), which is notoriously difficult otherwise. For observed data, a mixture of deterministic (signal) and stochastic (noise) parts is to be expected. We use an ensemble of longterm river runoff time series as example, which are known to exhibit powerlaw decaying longrange correlations. We compare these data with a longrange correlated candidate process, the k noise, from the perspective of order statistics and the CECP. Although these processes resemble runoff series in their correlation behavior and may be even tuned to any runoff series by changing the value of k, the CECP locations and in particular the order pattern statistics reveals qualitative differences between them. We give a detailed account of these differences, and use them to conclude on the deterministic nature of the (shortterm) dynamics of the runoff time series. The proposed methodology also represents a stringent test bed for hydrological or other environmental models. http://dames.pik-potsdam.de/Abstracts.pdf
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Forfattere
Jari Vauhkonen Liviu Theodor Ene Sandeep Gupta Johannes Heinzel Johan Holmgren Juho Pitkänen Svein Solberg Yunsheng Wang Holger Weinacker Marius Hauglin Vegard Lien Petteri Packalén Terje Gobakken Barbara Koch Erik Næsset Timo Tokola Matti MaltamoSammendrag
Airborne laser scanning data and corresponding field data were acquired from boreal forests in Norway and Sweden, coniferous and broadleaved forests in Germany and tropical pulpwood plantations in Brazil. Treetop positions were extracted using six different algorithms developed in Finland, Germany, Norway and Sweden, and the accuracy of tree detection and height estimation was assessed. Furthermore, the weaknesses and strengths of the methods under different types of forest were analyzed. The results showed that forest structure strongly affected the performance of all algorithms. Particularly, the success of tree detection was found to be dependent on tree density and clustering. The differences in performance between methods were more pronounced for tree detection than for height estimation. The algorithms showed a slightly better performance in the conditions for which they were developed, while some could be adapted by different parameterization according to training with local data. The results of this study may help guiding the choice of method under different forest types and may be of great value for future refinement of the single-tree detection algorithms.