@article{fdi:010095806,
  title = {{S}patial and temporal predictions of mosquito potential breeding sites and densities : integration of satellite imagery, in-situ data, and process-based modeling},
  author = {{T}eillet, {C}laire and {D}evillers, {R}odolphe and {T}ran, {A}. and {M}arti, {R}enaud and {D}emarchi, {M}. and {C}atry, {T}hibault and {R}wagitinywa, {J}. and {R}estrepo, {J}. and {D}essay, {N}adine and {R}oux, {E}mmanuel},
  editor = {},
  language = {{ENG}},
  abstract = {{D}engue fever, primarily transmitted worldwide by the mosquito {A}edes aegypti, poses significant public health challenges in tropical and subtropical regions. {W}hile effective vector control is crucial in the absence of reliable dengue vaccines, traditional control methods face obstacles like mosquito resistance to insecticides and a very high cost. {B}y combining geospatial data, including satellite imagery, as descriptors, and entomological surveys as target variables in a {R}andom {F}orest model, we predicted the number of potential mosquito breeding sites, derived the associated environmental carrying capacity for larvae, and used the {A}rbocarto process-based model to predict {A}e.aegypti population densities in an urban region of {F}rench {G}uiana, {S}outh {A}merica. {O}ur findings highlight that remote sensing data may help predict the number of potential breeding sites over urban areas. {O}ur simulations indicate higher mosquito densities in urban residential areas and a strong spatial and temporal heterogeneity. {T}hese densities fluctuate according to intra-annual variations in temperature and precipitation, with higher densities associated with intermediate housing. {A} comparison with the conventional estimation of environmental carrying capacity for larvae in the current {A}rbocarto procedure highlights the advantages of our approach. {O}ur study demonstrates the utility of integrating remote sensing with predictive modeling to enhance vector surveillance and control strategies, and provides a replicable approach for monitoring a dengue vector mosquito population in dynamic urban landscapes.},
  keywords = {{A}edes ; machine learning ; remote sensing ; vector control ; population ; dynamics model. ; {GUYANE} {FRANCAISE} ; {CAYENNE} {ILE}},
  booktitle = {},
  journal = {{I}nternational {A}rchives of the {P}hotogrammetry, {R}emote {S}ensing and {S}patial {I}nformation {S}ciences},
  volume = {{XLVIII}-3-2024},
  numero = {},
  pages = {539--545},
  ISSN = {1682-1750},
  year = {2024},
  DOI = {10.5194/isprs-archives-{XLVIII}-3-2024-539-2024},
  URL = {https://www.documentation.ird.fr/hor/fdi:010095806},
}