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Evaluating the Potential of Landscape Metrics in Supporting Landscape Planning in Atlantic Forest: Rio de Janeiro, Brazil

Evaluating the Potential of Landscape Metrics in Supporting Landscape Planning in Atlantic Forest: Rio de Janeiro, Brazil

Ana Paula Dias Turetta, Rachel Bardy Prado, Gustavo de Souza Valladares
Copyright: © 2013 |Volume: 4 |Issue: 1 |Pages: 13
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781466631724|DOI: 10.4018/jaeis.2013010104
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MLA

Turetta, Ana Paula Dias, et al. "Evaluating the Potential of Landscape Metrics in Supporting Landscape Planning in Atlantic Forest: Rio de Janeiro, Brazil." IJAEIS vol.4, no.1 2013: pp.55-67. http://doi.org/10.4018/jaeis.2013010104

APA

Turetta, A. P., Prado, R. B., & Valladares, G. D. (2013). Evaluating the Potential of Landscape Metrics in Supporting Landscape Planning in Atlantic Forest: Rio de Janeiro, Brazil. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 4(1), 55-67. http://doi.org/10.4018/jaeis.2013010104

Chicago

Turetta, Ana Paula Dias, Rachel Bardy Prado, and Gustavo de Souza Valladares. "Evaluating the Potential of Landscape Metrics in Supporting Landscape Planning in Atlantic Forest: Rio de Janeiro, Brazil," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 4, no.1: 55-67. http://doi.org/10.4018/jaeis.2013010104

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Abstract

The landscapes are highly dependent on the dynamics of local land use and land cover, which directly affects landscape structure and determines the spatial patterns of forest patches, as well as to the major land uses within a specific region. The calculation of landscape metrics can support the understanding of such spatial distribution. In this study, 16 landscape metrics were analyzed in a drainage watershed in a high relief region in the Rio de Janeiro state, Southeastern Brazil, with the aim to evaluate the use of landscape metrics as indicators for agricultural management. Metrics calculation was followed by a Principal Component Analysis, which indicated the metrics that were most effective in evidencing the landscape structure in analysis. The results showed that the late-succession forest is the dominant component in the landscape. This class also presented the highest MPS metric value, related to the mean patch size by class. Some PCA results suggest that the metrics association was less effective in clustering the overgrown pasture, clean pasture, and annual crops classes, but this could result from the intrinsic association among those classes, by crop rotation, meaning the abandon of a site formerly occupied by an annual crop. Some metrics better suggested an interaction among land use classes and have potential to be use in the analyses of agricultural landscapes in high relief sites.

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