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Rodrigo Nogueira de Vasconcelos

Researcher at State University of Feira de Santana

Publications -  20
Citations -  214

Rodrigo Nogueira de Vasconcelos is an academic researcher from State University of Feira de Santana. The author has contributed to research in topics: Biodiversity & Species richness. The author has an hindex of 5, co-authored 15 publications receiving 93 citations. Previous affiliations of Rodrigo Nogueira de Vasconcelos include Federal University of Bahia.

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Indirect effects of habitat loss via habitat fragmentation: A cross-taxa analysis of forest-dependent species

Thomas Püttker, +78 more
TL;DR: It is argued that dismissing habitat fragmentation as a powerful force driving species extinction in tropical forest landscapes is premature and unsafe.
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Oil Spill Detection and Mapping: A 50-Year Bibliometric Analysis

TL;DR: The results suggest that the detection of oil in the sea has undergone a great evolution in the last decades and there is a strong relationship between the technological evolution aimed at detection with the improvement of remote sensing data acquisition methods.
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Atlantic butterflies: a data set of fruit-feeding butterfly communities from the Atlantic forests.

TL;DR: This data set represents a major effort to compile inventories of fruit-feeding butterfly communities, filling a knowledge gap about the diversity and distribution of these butterflies in the Atlantic Forest and is hoped to provide guidelines for future studies and planning of new inventories in this biome.
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What is the value of eucalyptus monocultures for the biodiversity of the Atlantic forest? A multitaxa study in southern Bahia, Brazil

TL;DR: Findings of the present study point out a moderate capacity of eucalyptus monocultures to harbor species of the forest fauna even when fully grown but highlights the opportunity that they might offer for increasing connectivity in anthropogenic forest landscapes depending on their management.
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SAR Oil Spill Detection System through Random Forest Classifiers

TL;DR: In this paper, a set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset.