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| Carmenta, Rachel; Lancaster Environment Centre; Centre for International Forestry Research (CIFOR); rcarmenta@hotmail.com; Parry, Luke; Lancaster Environment Centre; lukeparry1@gmail.com; Blackburn, Alan; Lancaster Environment Centre; alan.blackburn@lancaster.ac.uk; Vermeylen, Saskia; Lancaster Environment Centre; s.vermeylen@lancaster.ac.uk; Barlow, Jos; Lancaster Environment Centre; josbarlow@gmail.com. |
| Fire in the forested tropics has profound environmental, economic, and social impacts at multiple geographical scales. Causes of tropical fires are widely documented, although research contributions are from many disciplines, and each tends to focus on specific facets of a research problem, which might limit understanding of fire as a complex social-ecological system. We conducted a systematic review to (1) examine geographic and methodological focus in tropical fire research; (2) identify which types of landholders are the focus of the research effort; (3) test for a research method effect on the variables, e.g., socio-political, economic, and climatic, identified as causes of and proposed management solutions to tropical fire; and (4) examine... |
| Tipo: Peer-Reviewed Synthesis |
Palavras-chave: Fire management; Interdisciplinary research; Multiscale analysis; Scale-pattern-process; Social-ecological systems; Tropical forests. |
| Ano: 2011 |
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| Fablet, Ronan; Bouthemy, P. |
| A new approach for motion characterization in image sequences is presented. It relies on the probabilistic modeling of temporal and scale co-occurrence distributions of local motion-related measurements directly computed over image sequences. Temporal multiscale Gibbs models allow us to handle both spatial and temporal aspects of image motion content within a unified statistical framework. Since this modeling mainly involves the scalar product between co-occurrence values and Gibbs potentials, we can formulate and address several fundamental issues: model estimation according to the ML criterion (hence, model training and learning) and motion classification. We have conducted motion recognition experiments over a large set of real image sequences... |
| Tipo: Text |
Palavras-chave: Nonparametric motion analysis; Motion recognition; Multiscale analysis; Gibbs models; Co occurrences; ML criterion. |
| Ano: 2003 |
URL: http://archimer.ifremer.fr/doc/00000/10714/9323.pdf |
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