Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites
Background: The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models. Methods: We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available. Results: Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available. Conclusion: This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.
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Biblioteca del CIRAD Francia |
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L72 - Organismes nuisibles des animaux télédétection Aedes aegypti vecteur de maladie paysage imagerie par satellite zone urbaine environnement urbain Aedes méthode statistique urbanisation distribution spatiale site de reproduction http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_30482 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_8085 http://aims.fao.org/aos/agrovoc/c_29062 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_8088 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_295fbbb3 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 L72 - Organismes nuisibles des animaux télédétection Aedes aegypti vecteur de maladie paysage imagerie par satellite zone urbaine environnement urbain Aedes méthode statistique urbanisation distribution spatiale site de reproduction http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_30482 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_8085 http://aims.fao.org/aos/agrovoc/c_29062 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_8088 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_295fbbb3 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
spellingShingle |
L72 - Organismes nuisibles des animaux télédétection Aedes aegypti vecteur de maladie paysage imagerie par satellite zone urbaine environnement urbain Aedes méthode statistique urbanisation distribution spatiale site de reproduction http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_30482 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_8085 http://aims.fao.org/aos/agrovoc/c_29062 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_8088 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_295fbbb3 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 L72 - Organismes nuisibles des animaux télédétection Aedes aegypti vecteur de maladie paysage imagerie par satellite zone urbaine environnement urbain Aedes méthode statistique urbanisation distribution spatiale site de reproduction http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_30482 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_8085 http://aims.fao.org/aos/agrovoc/c_29062 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_8088 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_295fbbb3 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Teillet, Claire Devillers, Rodolphe Tran, Annelise Catry, Thibault Marti, Renaud Dessay, Nadine Rwagitinywa, Joseph Restrepo, Johana Roux, Emmanuel Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites |
description |
Background: The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models. Methods: We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available. Results: Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available. Conclusion: This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies. |
format |
article |
topic_facet |
L72 - Organismes nuisibles des animaux télédétection Aedes aegypti vecteur de maladie paysage imagerie par satellite zone urbaine environnement urbain Aedes méthode statistique urbanisation distribution spatiale site de reproduction http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_30482 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_8085 http://aims.fao.org/aos/agrovoc/c_29062 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_8088 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_295fbbb3 http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 |
author |
Teillet, Claire Devillers, Rodolphe Tran, Annelise Catry, Thibault Marti, Renaud Dessay, Nadine Rwagitinywa, Joseph Restrepo, Johana Roux, Emmanuel |
author_facet |
Teillet, Claire Devillers, Rodolphe Tran, Annelise Catry, Thibault Marti, Renaud Dessay, Nadine Rwagitinywa, Joseph Restrepo, Johana Roux, Emmanuel |
author_sort |
Teillet, Claire |
title |
Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites |
title_short |
Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites |
title_full |
Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites |
title_fullStr |
Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites |
title_full_unstemmed |
Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites |
title_sort |
exploring fine-scale urban landscapes using satellite data to predict the distribution of aedes mosquito breeding sites |
url |
http://agritrop.cirad.fr/610084/ http://agritrop.cirad.fr/610084/1/2024_Teillet_UrbanLandscapes_Satellite_Aedes.pdf |
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dig-cirad-fr-6100842024-08-02T14:54:58Z http://agritrop.cirad.fr/610084/ http://agritrop.cirad.fr/610084/ Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites. Teillet Claire, Devillers Rodolphe, Tran Annelise, Catry Thibault, Marti Renaud, Dessay Nadine, Rwagitinywa Joseph, Restrepo Johana, Roux Emmanuel. 2024. International Journal of Health Geographics, 23:18, 20 p.https://doi.org/10.1186/s12942-024-00378-3 <https://doi.org/10.1186/s12942-024-00378-3> Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites Teillet, Claire Devillers, Rodolphe Tran, Annelise Catry, Thibault Marti, Renaud Dessay, Nadine Rwagitinywa, Joseph Restrepo, Johana Roux, Emmanuel eng 2024 International Journal of Health Geographics L72 - Organismes nuisibles des animaux télédétection Aedes aegypti vecteur de maladie paysage imagerie par satellite zone urbaine environnement urbain Aedes méthode statistique urbanisation distribution spatiale site de reproduction http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_30482 http://aims.fao.org/aos/agrovoc/c_8164 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_8085 http://aims.fao.org/aos/agrovoc/c_29062 http://aims.fao.org/aos/agrovoc/c_146 http://aims.fao.org/aos/agrovoc/c_7377 http://aims.fao.org/aos/agrovoc/c_8088 http://aims.fao.org/aos/agrovoc/c_36230 http://aims.fao.org/aos/agrovoc/c_295fbbb3 Guyane française France http://aims.fao.org/aos/agrovoc/c_3093 http://aims.fao.org/aos/agrovoc/c_3081 Background: The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models. Methods: We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available. Results: Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available. Conclusion: This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/610084/1/2024_Teillet_UrbanLandscapes_Satellite_Aedes.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1186/s12942-024-00378-3 10.1186/s12942-024-00378-3 info:eu-repo/semantics/altIdentifier/doi/10.1186/s12942-024-00378-3 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1186/s12942-024-00378-3 info:eu-repo/semantics/reference/purl/https://forge.ird.fr/espace-dev/personnels/teillet/aedes_breeding_sites_modelling.git info:eu-repo/grantAgreement/EC////(EU) INTERREG/ info:eu-repo/grantAgreement/EC////(EU) Projet de coopération Régionale pour l'Observation des GuYanes par SATellite/PROGYSAT |