There is renewed interest in disease surveillance due to the persistence of numerous vector-borne diseases. Though the life-history processes of disease vectors are known to dictate their distribution patterns, it has been hypothesized that vectors, such as local mosquito abundance, could be predicted from factors such as climate and land use/land cover. Remote sensing has been used as a supplement to traditional methods of mosquito surveillance and to map the spatial distribution of mosquito habitats. This, in turn, affords optimism for improved vector/disease management to identify the best use of remote sensing data and geo-statistical techniques to predict and map the spatial distribution of mosquitoes, or, possibly, West Nile virus vectors in Harrison County, Mississippi. This study site was chosen because of the availability of mosquito data and due to a lack of such studies in the region. A number of spatial variables, such as normalized difference vegetation index (NDVI), tasseled cap transformation, land use/land cover data (LULC), precipitation data, digital elevation models (DEM), and soil data, along with mosquito counts and geo-statistical analysis ( e.g. spatial interpolation and geographic weighted regression (GWR), were used to predict mosquito abundance and to map possible West Nile virus vector distribution and abundance. A raster data model with 30m x 30m cell size was used to explore research questions. Results indicate NDVI, soil, and DEM are the most effective variables for predicting mosquito density and distribution. However, this does not exclude the use of tasseled cap and land cover data in certain situations as both were useful in predicting mosquito counts/density in July. To better observe yearly and seasonal patterns in mosquito counts as well as the error produced by interpolation techniques using mosquito count data, the need for data analysis over longer periods of time becomes quite apparent. The links between mosquito distribution and environmental factors is recognized in the results of this project though the details behind this relationship remain unclear. Though some techniques ( e.g., IDW and co-kriging) are shown to improve mosquito monitoring efforts, potential costs associated with the inclusion of remote sensing data may outweigh the benefit.
","abstract_html":"<p>There is renewed interest in disease surveillance due to the persistence of numerous vector-borne diseases. Though the life-history processes of disease vectors are known to dictate their distribution patterns, it has been hypothesized that vectors, such as local mosquito abundance, could be predicted from factors such as climate and land use/land cover. Remote sensing has been used as a supplement to traditional methods of mosquito surveillance and to map the spatial distribution of mosquito habitats. This, in turn, affords optimism for improved vector/disease management to identify the best use of remote sensing data and geo-statistical techniques to predict and map the spatial distribution of mosquitoes, or, possibly, West Nile virus vectors in Harrison County, Mississippi. This study site was chosen because of the availability of mosquito data and due to a lack of such studies in the region. A number of spatial variables, such as normalized difference vegetation index (NDVI), tasseled cap transformation, land use/land cover data (LULC), precipitation data, digital elevation models (DEM), and soil data, along with mosquito counts and geo-statistical analysis ( e.g. spatial interpolation and geographic weighted regression (GWR), were used to predict mosquito abundance and to map possible West Nile virus vector distribution and abundance. A raster data model with 30m x 30m cell size was used to explore research questions. Results indicate NDVI, soil, and DEM are the most effective variables for predicting mosquito density and distribution. However, this does not exclude the use of tasseled cap and land cover data in certain situations as both were useful in predicting mosquito counts/density in July. To better observe yearly and seasonal patterns in mosquito counts as well as the error produced by interpolation techniques using mosquito count data, the need for data analysis over longer periods of time becomes quite apparent. The links between mosquito distribution and environmental factors is recognized in the results of this project though the details behind this relationship remain unclear. Though some techniques ( e.g., IDW and co-kriging) are shown to improve mosquito monitoring efforts, potential costs associated with the inclusion of remote sensing data may outweigh the benefit.</p>","abstract_has_math":false,"creators":["Moore, Brian Matthew"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Masters Thesis","degree_discipline":"Geography and Geology","degree_department":null,"school":null,"contributors":["Bandana Kar","George Raber","Donald Yee"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-08-01T07:00:00Z","date_published":"2011-08-01T07:00:00Z","updated_at":"2026-07-24T05:44:58Z","subjects":["Geography","Remote Sensing","Social and Behavioral Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://aquila.usm.edu/masters_theses/476","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Bandana Kar","George Raber","Donald Yee"]},{"key":"dc:creator","label":"Author","values":["Moore, Brian Matthew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-11-09T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Geography and Geology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Geography","Remote Sensing","Social and Behavioral Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://aquila.usm.edu/masters_theses/476"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["There is renewed interest in disease surveillance due to the persistence of numerous vector-borne diseases. Though the life-history processes of disease vectors are known to dictate their distribution patterns, it has been hypothesized that vectors, such as local mosquito abundance, could be predicted from factors such as climate and land use/land cover. Remote sensing has been used as a supplement to traditional methods of mosquito surveillance and to map the spatial distribution of mosquito habitats. This, in turn, affords optimism for improved vector/disease management to identify the best use of remote sensing data and geo-statistical techniques to predict and map the spatial distribution of mosquitoes, or, possibly, West Nile virus vectors in Harrison County, Mississippi. This study site was chosen because of the availability of mosquito data and due to a lack of such studies in the region. A number of spatial variables, such as normalized difference vegetation index (NDVI), tasseled cap transformation, land use/land cover data (LULC), precipitation data, digital elevation models (DEM), and soil data, along with mosquito counts and geo-statistical analysis ( e.g. spatial interpolation and geographic weighted regression (GWR), were used to predict mosquito abundance and to map possible West Nile virus vector distribution and abundance. A raster data model with 30m x 30m cell size was used to explore research questions. Results indicate NDVI, soil, and DEM are the most effective variables for predicting mosquito density and distribution. However, this does not exclude the use of tasseled cap and land cover data in certain situations as both were useful in predicting mosquito counts/density in July. To better observe yearly and seasonal patterns in mosquito counts as well as the error produced by interpolation techniques using mosquito count data, the need for data analysis over longer periods of time becomes quite apparent. The links between mosquito distribution and environmental factors is recognized in the results of this project though the details behind this relationship remain unclear. Though some techniques ( e.g., IDW and co-kriging) are shown to improve mosquito monitoring efforts, potential costs associated with the inclusion of remote sensing data may outweigh the benefit.
"]},{"key":"dc:title","label":"Title","values":["The Use of Remote Sensing for Mapping Possible Vectors of West Nile Virus in Mississippi"]}]}],"canonical_facts":{"dc:contributor":["Bandana Kar","George Raber","Donald Yee"],"dc:creator":["Moore, Brian Matthew"],"dc:date.available":["2018-11-09T08:00:00Z"],"dc:description.abstract":["There is renewed interest in disease surveillance due to the persistence of numerous vector-borne diseases. Though the life-history processes of disease vectors are known to dictate their distribution patterns, it has been hypothesized that vectors, such as local mosquito abundance, could be predicted from factors such as climate and land use/land cover. Remote sensing has been used as a supplement to traditional methods of mosquito surveillance and to map the spatial distribution of mosquito habitats. This, in turn, affords optimism for improved vector/disease management to identify the best use of remote sensing data and geo-statistical techniques to predict and map the spatial distribution of mosquitoes, or, possibly, West Nile virus vectors in Harrison County, Mississippi. This study site was chosen because of the availability of mosquito data and due to a lack of such studies in the region. A number of spatial variables, such as normalized difference vegetation index (NDVI), tasseled cap transformation, land use/land cover data (LULC), precipitation data, digital elevation models (DEM), and soil data, along with mosquito counts and geo-statistical analysis ( e.g. spatial interpolation and geographic weighted regression (GWR), were used to predict mosquito abundance and to map possible West Nile virus vector distribution and abundance. A raster data model with 30m x 30m cell size was used to explore research questions. Results indicate NDVI, soil, and DEM are the most effective variables for predicting mosquito density and distribution. However, this does not exclude the use of tasseled cap and land cover data in certain situations as both were useful in predicting mosquito counts/density in July. To better observe yearly and seasonal patterns in mosquito counts as well as the error produced by interpolation techniques using mosquito count data, the need for data analysis over longer periods of time becomes quite apparent. The links between mosquito distribution and environmental factors is recognized in the results of this project though the details behind this relationship remain unclear. Though some techniques ( e.g., IDW and co-kriging) are shown to improve mosquito monitoring efforts, potential costs associated with the inclusion of remote sensing data may outweigh the benefit.
"],"dc:identifier":["https://aquila.usm.edu/masters_theses/476"],"dc:subject":["Geography","Remote Sensing","Social and Behavioral Sciences"],"dc:title":["The Use of Remote Sensing for Mapping Possible Vectors of West Nile Virus in Mississippi"],"thesis:degree_discipline":["Geography and Geology"],"thesis:degree_level":["Masters Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T05:44:58Z"}