The Experts below are selected from a list of 10431 Experts worldwide ranked by ideXlab platform

Jean-michel Vassal - One of the best experts on this subject based on the ideXlab platform.

  • Coupling historical prospection data and a remotely-sensed vegetation index for the Preventative Control of Desert locusts
    Basic and Applied Ecology, 2013
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Abstract Locusts are grasshopper species that exhibit phase polyphenism resulting in the expression of gregarious behaviors that favor the development of large devastating bands and swarms. Desert locust Preventative management aims to prevent crop damage by Controlling populations before they can reach high densities and form mass migrating swarms. The areas of potential gregarization for Desert locust are large and need to be physically assessed by survey teams for efficient Preventative management. An ongoing challenge is to be able to guide where prospection surveys should occur depending on local meteorological and vegetation conditions. In this study, we analyzed the relationship between historical prospection data of Desert locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing with the help of multiple models of logistic regression. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250 m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: (1) spatial means at different scales around the prospection point, (2) relative differences of NDVI variation through time before the prospection, and (3) large-scale summary of vegetation quantity. The multi-model framework showed that vegetation development a month and a half before the survey was amongst the best predictors of locust presence. Also, the local vegetation quantity was not enough to predict locust presence. Vegetation quantity on a scale of a few kilometers was a better predictor but varied non-linearly, reflecting specific biotope types that support Desert locust development. Using one of the best logistic regression models and NDVI data, we were able to derive a predictive model of probability of finding locusts in specific areas. This methodology should help in more efficiently focusing survey efforts on specific parts of the gregarization areas based on the predicted probability of locusts being present.

  • Linking vegetation indexes from remote sensing and long-term prospection data to help in the Preventative Control of desert locust : O401M19 IPM
    2012
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Jean-michel Vassal, Michel Lecoq
    Abstract:

    Desert Locust management is nowadays done through a Preventative Control strategy that consists in avoiding populations to reach high densities and a gregarization process leading to unControllable swarms. The areas of potential start of the gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using multiple logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of adequate logistic regression models, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Resume d'auteur)

  • Coupling long-term prospection data and remote-sensing vegetation index to help in the Preventative Control of Desert Locust
    2011
    Co-Authors: Cyril Piou, Valentine Lebourgeois, Michel Lecoq, Ahmed Salem Benahi, Vincent Bonal, Mohamed El Hacen Jaavar, Jean-michel Vassal
    Abstract:

    Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a Preventative Control avoiding population to reach high and unControllable densities. The areas of potential start of gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times.

  • Coupling long-term prospection data and remote-sensing vegetation index to help in the Preventative Control of Desert Locust
    2011
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a Preventative Control avoiding population to reach high and unControllable densities. The areas of potential start of gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Texte integral)

Cyril Piou - One of the best experts on this subject based on the ideXlab platform.

  • Coupling historical prospection data and a remotely-sensed vegetation index for the Preventative Control of Desert locusts
    Basic and Applied Ecology, 2013
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Abstract Locusts are grasshopper species that exhibit phase polyphenism resulting in the expression of gregarious behaviors that favor the development of large devastating bands and swarms. Desert locust Preventative management aims to prevent crop damage by Controlling populations before they can reach high densities and form mass migrating swarms. The areas of potential gregarization for Desert locust are large and need to be physically assessed by survey teams for efficient Preventative management. An ongoing challenge is to be able to guide where prospection surveys should occur depending on local meteorological and vegetation conditions. In this study, we analyzed the relationship between historical prospection data of Desert locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing with the help of multiple models of logistic regression. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250 m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: (1) spatial means at different scales around the prospection point, (2) relative differences of NDVI variation through time before the prospection, and (3) large-scale summary of vegetation quantity. The multi-model framework showed that vegetation development a month and a half before the survey was amongst the best predictors of locust presence. Also, the local vegetation quantity was not enough to predict locust presence. Vegetation quantity on a scale of a few kilometers was a better predictor but varied non-linearly, reflecting specific biotope types that support Desert locust development. Using one of the best logistic regression models and NDVI data, we were able to derive a predictive model of probability of finding locusts in specific areas. This methodology should help in more efficiently focusing survey efforts on specific parts of the gregarization areas based on the predicted probability of locusts being present.

  • Linking vegetation indexes from remote sensing and long-term prospection data to help in the Preventative Control of desert locust : O401M19 IPM
    2012
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Jean-michel Vassal, Michel Lecoq
    Abstract:

    Desert Locust management is nowadays done through a Preventative Control strategy that consists in avoiding populations to reach high densities and a gregarization process leading to unControllable swarms. The areas of potential start of the gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using multiple logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of adequate logistic regression models, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Resume d'auteur)

  • Coupling long-term prospection data and remote-sensing vegetation index to help in the Preventative Control of Desert Locust
    2011
    Co-Authors: Cyril Piou, Valentine Lebourgeois, Michel Lecoq, Ahmed Salem Benahi, Vincent Bonal, Mohamed El Hacen Jaavar, Jean-michel Vassal
    Abstract:

    Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a Preventative Control avoiding population to reach high and unControllable densities. The areas of potential start of gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times.

  • Coupling long-term prospection data and remote-sensing vegetation index to help in the Preventative Control of Desert Locust
    2011
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a Preventative Control avoiding population to reach high and unControllable densities. The areas of potential start of gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Texte integral)

Michel Lecoq - One of the best experts on this subject based on the ideXlab platform.

  • Coupling historical prospection data and a remotely-sensed vegetation index for the Preventative Control of Desert locusts
    Basic and Applied Ecology, 2013
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Abstract Locusts are grasshopper species that exhibit phase polyphenism resulting in the expression of gregarious behaviors that favor the development of large devastating bands and swarms. Desert locust Preventative management aims to prevent crop damage by Controlling populations before they can reach high densities and form mass migrating swarms. The areas of potential gregarization for Desert locust are large and need to be physically assessed by survey teams for efficient Preventative management. An ongoing challenge is to be able to guide where prospection surveys should occur depending on local meteorological and vegetation conditions. In this study, we analyzed the relationship between historical prospection data of Desert locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing with the help of multiple models of logistic regression. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250 m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: (1) spatial means at different scales around the prospection point, (2) relative differences of NDVI variation through time before the prospection, and (3) large-scale summary of vegetation quantity. The multi-model framework showed that vegetation development a month and a half before the survey was amongst the best predictors of locust presence. Also, the local vegetation quantity was not enough to predict locust presence. Vegetation quantity on a scale of a few kilometers was a better predictor but varied non-linearly, reflecting specific biotope types that support Desert locust development. Using one of the best logistic regression models and NDVI data, we were able to derive a predictive model of probability of finding locusts in specific areas. This methodology should help in more efficiently focusing survey efforts on specific parts of the gregarization areas based on the predicted probability of locusts being present.

  • Linking vegetation indexes from remote sensing and long-term prospection data to help in the Preventative Control of desert locust : O401M19 IPM
    2012
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Jean-michel Vassal, Michel Lecoq
    Abstract:

    Desert Locust management is nowadays done through a Preventative Control strategy that consists in avoiding populations to reach high densities and a gregarization process leading to unControllable swarms. The areas of potential start of the gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using multiple logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of adequate logistic regression models, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Resume d'auteur)

  • Coupling long-term prospection data and remote-sensing vegetation index to help in the Preventative Control of Desert Locust
    2011
    Co-Authors: Cyril Piou, Valentine Lebourgeois, Michel Lecoq, Ahmed Salem Benahi, Vincent Bonal, Mohamed El Hacen Jaavar, Jean-michel Vassal
    Abstract:

    Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a Preventative Control avoiding population to reach high and unControllable densities. The areas of potential start of gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times.

  • Coupling long-term prospection data and remote-sensing vegetation index to help in the Preventative Control of Desert Locust
    2011
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a Preventative Control avoiding population to reach high and unControllable densities. The areas of potential start of gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Texte integral)

Valentine Lebourgeois - One of the best experts on this subject based on the ideXlab platform.

  • Coupling historical prospection data and a remotely-sensed vegetation index for the Preventative Control of Desert locusts
    Basic and Applied Ecology, 2013
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Abstract Locusts are grasshopper species that exhibit phase polyphenism resulting in the expression of gregarious behaviors that favor the development of large devastating bands and swarms. Desert locust Preventative management aims to prevent crop damage by Controlling populations before they can reach high densities and form mass migrating swarms. The areas of potential gregarization for Desert locust are large and need to be physically assessed by survey teams for efficient Preventative management. An ongoing challenge is to be able to guide where prospection surveys should occur depending on local meteorological and vegetation conditions. In this study, we analyzed the relationship between historical prospection data of Desert locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing with the help of multiple models of logistic regression. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250 m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: (1) spatial means at different scales around the prospection point, (2) relative differences of NDVI variation through time before the prospection, and (3) large-scale summary of vegetation quantity. The multi-model framework showed that vegetation development a month and a half before the survey was amongst the best predictors of locust presence. Also, the local vegetation quantity was not enough to predict locust presence. Vegetation quantity on a scale of a few kilometers was a better predictor but varied non-linearly, reflecting specific biotope types that support Desert locust development. Using one of the best logistic regression models and NDVI data, we were able to derive a predictive model of probability of finding locusts in specific areas. This methodology should help in more efficiently focusing survey efforts on specific parts of the gregarization areas based on the predicted probability of locusts being present.

  • Linking vegetation indexes from remote sensing and long-term prospection data to help in the Preventative Control of desert locust : O401M19 IPM
    2012
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Jean-michel Vassal, Michel Lecoq
    Abstract:

    Desert Locust management is nowadays done through a Preventative Control strategy that consists in avoiding populations to reach high densities and a gregarization process leading to unControllable swarms. The areas of potential start of the gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using multiple logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of adequate logistic regression models, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Resume d'auteur)

  • Coupling long-term prospection data and remote-sensing vegetation index to help in the Preventative Control of Desert Locust
    2011
    Co-Authors: Cyril Piou, Valentine Lebourgeois, Michel Lecoq, Ahmed Salem Benahi, Vincent Bonal, Mohamed El Hacen Jaavar, Jean-michel Vassal
    Abstract:

    Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a Preventative Control avoiding population to reach high and unControllable densities. The areas of potential start of gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times.

  • Coupling long-term prospection data and remote-sensing vegetation index to help in the Preventative Control of Desert Locust
    2011
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a Preventative Control avoiding population to reach high and unControllable densities. The areas of potential start of gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Texte integral)

Ahmed Salem Benahi - One of the best experts on this subject based on the ideXlab platform.

  • Coupling historical prospection data and a remotely-sensed vegetation index for the Preventative Control of Desert locusts
    Basic and Applied Ecology, 2013
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Abstract Locusts are grasshopper species that exhibit phase polyphenism resulting in the expression of gregarious behaviors that favor the development of large devastating bands and swarms. Desert locust Preventative management aims to prevent crop damage by Controlling populations before they can reach high densities and form mass migrating swarms. The areas of potential gregarization for Desert locust are large and need to be physically assessed by survey teams for efficient Preventative management. An ongoing challenge is to be able to guide where prospection surveys should occur depending on local meteorological and vegetation conditions. In this study, we analyzed the relationship between historical prospection data of Desert locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing with the help of multiple models of logistic regression. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250 m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: (1) spatial means at different scales around the prospection point, (2) relative differences of NDVI variation through time before the prospection, and (3) large-scale summary of vegetation quantity. The multi-model framework showed that vegetation development a month and a half before the survey was amongst the best predictors of locust presence. Also, the local vegetation quantity was not enough to predict locust presence. Vegetation quantity on a scale of a few kilometers was a better predictor but varied non-linearly, reflecting specific biotope types that support Desert locust development. Using one of the best logistic regression models and NDVI data, we were able to derive a predictive model of probability of finding locusts in specific areas. This methodology should help in more efficiently focusing survey efforts on specific parts of the gregarization areas based on the predicted probability of locusts being present.

  • Linking vegetation indexes from remote sensing and long-term prospection data to help in the Preventative Control of desert locust : O401M19 IPM
    2012
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Jean-michel Vassal, Michel Lecoq
    Abstract:

    Desert Locust management is nowadays done through a Preventative Control strategy that consists in avoiding populations to reach high densities and a gregarization process leading to unControllable swarms. The areas of potential start of the gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using multiple logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of adequate logistic regression models, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Resume d'auteur)

  • Coupling long-term prospection data and remote-sensing vegetation index to help in the Preventative Control of Desert Locust
    2011
    Co-Authors: Cyril Piou, Ahmed Salem Benahi, Vincent Bonnal, Mohamed El Hacen Jaavar, Valentine Lebourgeois, Michel Lecoq, Jean-michel Vassal
    Abstract:

    Prospection data are generally collected in oriented manner and toward the immediate needs of pest management. Despite the evident statistical bias these data present, when coupled with external indicators of environmental status, prospection data can help in characterizing interesting relationships between the focused pest and its environment. Desert Locust management is generally done through a Preventative Control avoiding population to reach high and unControllable densities. The areas of potential start of gregarization process for Desert Locust are large and Preventative management teams need to prospect all these areas to be efficient. A challenge of ongoing research is to be able to guide on where prospection surveys should be done depending on meteorological and vegetation conditions. An analysis of relationship between long-term prospection data of Desert Locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing was conducted using logistic regressions. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: 1) spatial means at different scales around the prospection point, 2) relative differences of NDVI variation through time before the prospection and 3) large scale summary of vegetation quality. Identical statistics could potentially be computed for actual NDVI. By extrapolation of the chosen logistic regression model, maps of probability of presence of locust could be constructed. This methodology should help in focusing prospection toward sensible parts of the gregarization areas at specific times. (Texte integral)