@Article{SilvaPASSSAFBS:2019:ClAnIn,
author = "Silva, Vilena Aparecida Ribeiro and Portela, Larissa Brand{\~a}o
and Almeida, Juliana Lopes and Silva J{\'u}nior, Celso Henrique
Leite and Santos, Juliana Sales dos and Santos, Jessflan Rafael
Nascimento and Araujo, Mayara Lucyanne Santos de and Feitosa,
Francisco Emenson Carpegiane Silva and Bezerra, C{\'{\i}}cero
Wellington Brito and Silva, Fabr{\'{\i}}cio Brito",
affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e
Tecnologia do Maranh{\~a}o and {Universidade Federal do
Maranh{\~a}o (UFMA)} and {Universidade CEUMA} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidade CEUMA}
and {Universidade CEUMA} and {Universidade Federal de Campina
Grande (UFCG)} and {Universidade CEUMA} and {Universidade Federal
do Maranh{\~a}o (UFMA)} and {Universidade CEUMA}",
title = "Climatic and Anthropic Influence on the Geodiversity of the
Maranh{\~a}o Amazon Floodplain",
journal = "Journal of Agricultural Science",
year = "2019",
volume = "11",
pages = "105",
keywords = "deforestation, fires, NDVI, Pinheiro, Ramsar, remote sensing.",
abstract = "The Maranhense Amazon floodplain shelters a Ramsar site
established by the United Nations for the protection of wetland
biodiversity. Despite its protected ecological status, the impacts
from deforestation, burning, the agricultural and livestock
industries, are on the rise. Knowledge of the spatial distribution
and temporal dynamics of these impacts are important to improve
the understanding of how this region is affected. Data on
increasing deforestation and hot pixels were used to evaluate the
anthropogenic pressure under the geodiversity of the region,
relating them to the environmental variables (rainfall, Normalized
Difference Vegetation Index and Deforestation annual deforestation
rate) measured through the rainfall data and the Normalized
Difference Vegetation Index (NDVI). In this study, the potential
of remote sensing and geographic information system. The time
series were used from 2001 to 2016 for all variables. We observed
a strong negative and significant correlation between hot pixels
and NDVI, while hot pixels increase, the vegetation indexes tend
to decrease. In 2006 an abrupt fall in the NDVI occurred due to
the marked increase in the deforested area. In 2010, the NDVI
reached its highest levels, because the vegetation responded to
the highest rainfall observed in the period in 2009. Unit 4
presented the highest pixels number in the period evaluated (2,978
pixels; 55% of the total). There is a significant correlation
between NDVI and rainfall.",
doi = "10.5539/jas.v11n18p105",
url = "http://dx.doi.org/10.5539/jas.v11n18p105",
issn = "0021-8596",
label = "lattes: 0506851271452550 4 SilvaPASSSAFBS:2019:ClAnIn",
language = "en",
targetfile = "silva_climatic.pdf",
url = "http://www.ccsenet.org/journal/index.php/jas/article/view/0/41206",
urlaccessdate = "27 abr. 2024"
}