@Article{LiCaDeCoAlBaRo:2017:ExStAm,
author = "Li, Zhichao and Catry, Thibault and Dessay, Nadine and Costa
Gurgel, Helen da and Almeida, Cl{\'a}udio Aparecido de and
Barcellos, Christovam and Roux, Emmanuel",
affiliation = "{Tsinghua University} and {Institut de Recherche pour le
D{\'e}veloppement (IRD)} and {Institut de Recherche pour le
D{\'e}veloppement (IRD)} and {Universidade de Bras{\'{\i}}lia
(UnB)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Funda{\c{c}}{\~a}o Oswaldo Cruz (FIOCRUZ)} and {Institut de
Recherche pour le D{\'e}veloppement (IRD)}",
title = "Regionalization of a landscape-based hazard index of malaria
transmission: an example of the state of Amap{\'a}, Brazil",
journal = "Data",
year = "2017",
volume = "2",
number = "4",
pages = "37",
keywords = "malaria, landscape-based hazard index, large-scale, Amazon.",
abstract = ": Identifying and assessing the relative effects of the numerous
determinants of malaria transmission, at different spatial scales
and resolutions, is of primary importance in defining control
strategies and reaching the goal of the elimination of malaria. In
this context, based on a knowledge-based model, a normalized
landscape-based hazard index (NLHI) was established at a local
scale, using a 10 m spatial resolution forest vs. non-forest map,
landscape metrics and a spatial moving window. Such an index
evaluates the contribution of landscape to the probability of
human-malaria vector encounters, and thus to malaria transmission
risk. Since the knowledge-based model is tailored to the entire
Amazon region, such an index might be generalized at large scales
for establishing a regional view of the landscape contribution to
malaria transmission. Thus, this study uses an open large-scale
land use and land cover dataset (i.e., the 30 m TerraClass maps)
and proposes an automatic data-processing chain for implementing
NLHI at large-scale. First, the impact of coarser spatial
resolution (i.e., 30 m) on NLHI values was studied. Second, the
data-processing chain was established using R language for
customizing the spatial moving window and computing the landscape
metrics and NLHI at large scale. This paper presents the results
in the State of Amap{\'a}, Brazil. It offers the possibility of
monitoring a significant determinant of malaria transmission at
regional scale.",
doi = "10.3390/data2040037",
url = "http://dx.doi.org/10.3390/data2040037",
issn = "2306-5729",
label = "lattes: 1240868188538349 5 LiCaDeCoApBaRo:2017:ExStAm",
language = "en",
targetfile = "data-02-00037-v2.pdf",
urlaccessdate = "27 abr. 2024"
}